The present invention relates generally to the electrical, electronic and computer arts, and, more particularly, to techniques for using a plurality of heterogeneous decision engines to produce a single decision.
Improving the accuracy and performance of decision support systems (DSS) remains a persistent challenge within the fields of machine learning and pattern recognition. There are often variety of decision engines for use in DSS, such as knowledge-based and/or data-based analysis modules, which typically generate one or more labels (e.g., textual descriptions of options) and corresponding values (e.g., a numeric representation of the support degree for each option). However, these decision engines are typically imperfect classifiers with varying strengths and weaknesses. Multiple classifier systems (MCS) combine different opinions from different decision engines to make a final decision in a process known as decision fusion. Combining the outputs from different decision engines allows all decision engines to contribute to the final decision, which is of higher quality than a decision generated by any single decision engine, which as noted above are typically imperfect classifiers. MCS is sometimes referred to as ensemble learning or mixture of experts.
However, a conventional MCS often requires each of its constituent classifiers to produce uniform (e.g. homogeneous) labels. Thus, a conventional MCS typically is limited to classifiers (e.g., decision engines) which were designed and constructed specifically for use in the MCS. Legacy decision engines which were previously developed and constructed for other purposes cannot be easily integrated within conventional MCSes because these decision engines may produce output values with non-uniform (e.g. heterogeneous) labels relative to each other. For example, legacy decision engines may refer to the same concept using different terms (e.g., synonyms) and/or one of the legacy decision engines may produce output values with labels having a different granularity than another of the legacy decision engines.